348 IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT, … · one willbuy[26].Psychological riskisdeﬁned asconcern about self-image that is reﬂected on the purchased product [53]. The

348 IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT, VOL. 59, NO. 3, AUGUST 2012

The Consumer Choice of E-Channels as a PurchasingAvenue: An Empirical Investigation of the

Communicative Aspects of Information QualityJaejoo Lim, Varun Grover, and Russell L. Purvis

Abstract—The vast majority of retail customers use electronicchannels (e-channels) to search for product information, but do notcomplete the purchasing process online. This research investigatesthe role of information quality in transitioning retail consumers tocomplete the purchasing process online, thereby replacing physicalchannels with online channels. This research makes three contri-butions: 1) it highlights the importance of information quality andits influence on a consumer’s choice to use e-channels to purchaseonline; 2) it supports the premise that high-quality information canconvert experience attributes into search attributes; and 3) it iden-tifies four antecedents that increase perceived information quality:higher telepresence, screening capability, channel trustworthiness,and lower cognitive overhead. Data from 309 consumers were an-alyzed using structural equation modeling and regression. Impli-cations and future research avenues are discussed.

Index Terms—Channel choice, e-business, e-commerce,e-commerce success, experiment, information quality, motivationtheory, structured equation modeling, telepresence.

I. INTRODUCTION

THE Internet is one of the largest technological innovationsto impact the retail industry. While the 2010 U.S. Cen-

sus [89] found online sales accounted for 3.2% (or 127 billiondollars of the retail sales in the U.S.), online sales are predicted toincrease to 8% by 2013 [9]. Considering 89% of consumers usethe Internet to search for product or service information [89], theimportance of electronic channels (e-channels) is critical to theretail industry. However, using the Internet to search for prod-uct information and using the Internet as a purchasing channelare quite different. We investigate what cause consumers to usee-channels beyond product information search, and completethe purchasing process online, thereby switching from physicalchannels to purchase products to the Internet.

A critical issue to consider is how well consumers are “. . .able to evaluate the characteristics of goods in the digital envi-ronment” [4, p. 272]. If online consumers can assess the value

Manuscript received November 23, 2009; revised July 6, 2010, December15, 2010, April 8, 2011, and June 23, 2011; accepted July 19, 2011. Date ofpublication September 22, 2011; date of current version July 13, 2012. Reviewof this manuscript was arranged by Department Editor T. Ravichandran.

J. Lim is with the South Carolina State University, Orangeburg, SC 29117-0001 USA (e-mail: [email protected]).

V. Grover and R. L. Purvis are with the Management Department, Clem-son University, Clemson, SC 29634-1305 USA (e-mail: [email protected];[email protected]).

Color versions of one or more of the figures in this paper are available onlineat http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/TEM.2011.2164802

and quality of a product on the Internet, they should be moreinclined to buy the product online due to additional benefitsoffered by e-channels (e.g., shopping convenience, wider se-lections, 24/7 access). We contend that information quality, fa-cilitated through information technology (IT), can enable suchassessments, including goods that customers like to experienceprior to purchase. While the importance of information has beenemphasized in the marketing and information systems (IS) liter-ature, interest in information quality on the Internet has been aninformation source. The study extends this to examine how in-formation quality supports using electronic channels beyond theinformation search process through purchasing. This researchmakes three contributions to the e-commerce literature. First,it highlights the importance of information quality and its in-fluence on a consumer’s choice to use e-channels to purchaseonline. Second, it supports the premise that high-quality infor-mation can convert experience attributes into search attributes.Finally, it identifies four important antecedents that increaseperceived information quality: 1) higher telepresence; 2) betterscreening capability; 3) higher channel trustworthiness; and 4)lower cognitive overhead. The next section synthesizes the ex-tant literature on e-channel choice and information quality, andintroduces the theoretical framework guiding this study. The re-search model and hypotheses are then described, followed bythe research method used and the results of the data analysis.Finally, the discussion, implications, and limitations of the studyare discussed, concluding with future research directions.

II. LITERATURE REVIEW AND SYNTHESIS

Research on what attract consumers to use e-channels hasused a variety of dependent variables (e.g., incentives [1], inten-tions [11], buying online [57], adoption [69], and commitment1 [32]) in diverse contexts, with different goals, and different def-initions and boundaries of “choice.” Three distinct perspectivesarise from previous research. These perspectives are discussednext, and the literature review on these three perspectives ofe-channel choice can be found in the Appendix.

A. Choice of E-Channels

In the first perspective, the focus is on the choice between in-dividual websites competing for customers. These studies haveunderlying assumptions on physical channel competition, and

1Our study targets people who have access to the Internet and have used theInternet for information searching and purchasing. See “Descriptive Statistics”in this paper.

0018-9391/$26.00 © 2011 IEEE

LIM et al.: CONSUMER CHOICE OF E-CHANNELS AS A PURCHASING AVENUE: AN EMPIRICAL INVESTIGATION 349

focus on competition among web vendors using characteristicssuch as trust [4], [28], [71], media coverage [66], website qual-ity [13], and hedonic factors [13], [28] to attract consumers. Thisperspective includes behavioral studies that focus on how to in-crease website traffic on competing websites [66]. Because thisresearch does not explicitly distinguish between website usesfor information search and product purchases, the usefulness ofthis perspective is limited for our study.

The second and most prevalent perspective is research onusing e-channels within the purchasing process. The dominantframework for this perspective is the technology acceptancemodel , including the theory of reasoned action [26]. This streamof research focuses on the salient beliefs about Internet technol-ogy (e.g., perceived ease of use and usefulness) and percep-tions about Internet technology such as trust [26] and perceivedrisk [5]. Marketing research has focused on utilitarian and he-donic factors [10] such as convenience and usefulness that haveproven particularly important in choosing to use e-commercesystems for purchasing. This perspective also offers limited in-sight for this study as it does not offer comparative explanationsabout the advantages and disadvantages between electronic andphysical channels for purchasing.

Finally, the third perspective considers choice of e-channelsover physical channels for product purchases, and considersthe comparative advantages and disadvantages of electronicchannels to physical channels. Factors that influence e-channelchoice identified within the disciplines of IT, marketing, andconsumer psychology, include convenience [11], [47], avail-able selection [37], price [34], product characteristics [2], [11],risk [27], and e-security [1], [11]. While this perspective of-fers a useful lens, it too offers limited insight for this researchas studies within this stream rarely distinguish between usinge-channels for product search and purchasing.

Research within these three perspectives has contributed tothe literature by setting guidelines and developing theoreticalframeworks. However, there are limitations for use in this study,including: 1) varied definitions of choice (e.g., between web-sites); 2) a myriad of factors in various perspectives that creategaps as well as overlap in developing a comprehensive under-standing; 3) different uses of e-channels (e.g., as an informationchannel or purchase channel); and 4) a lack of studies on infor-mation quality. Consequently, there is a need for research thatconsiders these factors through a broader nomological frame-work that incorporates both information search and purchasephases of the purchasing process. Further, there is a need toinvestigate the role of information quality as a link betweenproduct search and actual purchase on e-channels.

B. Information Quality

Traditional IS literature offers a rich amalgam of research oninformation quality that uses a variety of definitions and views ofthe construct [87]. The first view (presentational) was quite per-vasive during early studies within MIS focusing on the presen-tation, format, and graphic display of information in decision-making and learning behaviors, among others (e.g., [20]). Thesecond view, the intrinsic value of information quality, considers

whether there is “agreement between the data values presentedby an IS and the actual values the data represents in the realworld” [76, p. 202]. While useful, this view regards informationin isolation of the context in which it is applied [66]. In con-trast, the third view (contextual) emphasizes information qual-ity in relation to the context of the user, task, and applicationwithin which the information is used [47]. Since information isperceived in the online purchasing environment in this study,this research uses the more utilitarian contextual view. Usingthis view, information quality in e-channels is defined as the de-gree to which information in e-channels facilitates a consumer’sevaluation of products to complete online purchasing.

Many of the studies on information quality in e-channelshave focused on its measurement and are mostly derived fromZmud’s [92] seminal article about the dimensions of informationquality found in traditional information systems. Over the lastthree decades, these dimensions have been expanded and con-densed by several researchers (e.g., [87], [90]) that usually findaccuracy as the most important dimension. In the e-commerceliterature, there are a myriad of dimensions under the collectiverepresentational, intrinsic, and contextual views, with no agree-ment on what constitutes a complete, yet parsimonious set ofdimensions [66]. In addition, information quality in e-channelsneeds to incorporate sophisticated features offered by advancesin technologies incorporated into the Internet such as interactiveinformation extraction and audio-visual illustration [91]. Thenext section presents motivation as the underlying theoreticalframe for this study.

III. THEORETICAL FRAMEWORK

So why are consumers motivated to purchase over the Inter-net? Konorski [45] identified two mutually antagonistic motiva-tional components that offer a useful theoretical lens to considerwhat makes consumers seeking information on e-channels tocontinue through the purchase process. The motivational com-ponents are: 1) appetitive motivation that seeks benefits and2) aversive motivation that avoids or mitigates costs or risk.This stream of research suggests that choice behavior is a directresult of balancing the two motivational components by opti-mizing gratification and minimizing deprivation [19]. The ISand marketing literature is replete with factors and explanationsbased on the appetitive motivation (i.e., seeking benefits), suchas 1) lower search costs and price [11]; 2) convenience (e.g.,open 24/7); 3) eliminating travel time and costs [39]; 4) betteraccessibility to the products chosen in the information searchstage [56]; and 5) more product and service options [35].

Aversive motivation (risk avoidance behaviors) offers expla-nations for consumers who, initially attracted to e-channels bythe comparative benefits, may be reluctant to actually buy on-line because of the risks related to e-channels. Six types of riskin traditional retail channels (physical, social, psychological, fi-nancial, source, and performance) offer useful dimensions ofaversive motivation for this study. Physical risk is minimal asit pertains to one’s physical health and well-being [27], [53].Indeed, most consumers would consider online shopping to beless risky from a physical health perspective than purchasing


TABLE ICONSTRUCT DEFINITIONS

through a physical channel. Social risk arises from compliancepressure that is concerned with others’ opinion about the productone will buy [26]. Psychological risk is defined as concern aboutself-image that is reflected on the purchased product [53]. Theanonymity of purchasing online limits both social and psycho-logical risks, leaving financial, source, and performance riskspertinent to this study.

Financial risk2 concerns the potential overpayment for a prod-uct. This can be particularly acute within e-commerce due to thepotential hidden costs associated with receiving, returning, andmaintaining products [60]. However, high-quality services suchas free express delivery, order tracking, free return plans, andonline customer support should reduce perceptions of financialrisk. Source risk relates to the risk of whether a seller or retailchannel is a credible retail source [60]. For this study, focusingon the collective electronic channel, this type of risk is primarilya function of the reliability and security of the purchase chan-nel. Source risk includes concerns about unsafe transactions ine-channels such as abuse of private information, security issuesincluding identity theft, as well as threats posed by computerviruses [7]. Source risks can be addressed by high system qualityattainable through computer security and prevention efforts [50]as well as retailer guarantees of security and privacy and use ofsecure transaction systems (e.g., PayPal). Recent studies havedemonstrated the importance of both service quality [27] andsystem quality [2] in consumers’ choice of e-channels.3 There-fore, comparative benefits, service quality, and system qualityare known to affect choice e-channels and used as control vari-ables in this study.

Finally, performance risk concerns whether a purchased prod-uct will perform as expected and satisfy the consumer’s require-ments [67]. Many e-commerce researchers believe that due toperformance risk, most e-channel transactions will be relegated

2A time-loss risk from McCorkle’s [60] article is incorporated into a financialrisk because an express delivery is possible by paying a higher delivery fee.Javenpaa and Todd’s economic risk and personal risk correspond to McCorkle’sfinancial risk.

3The quality needs to be sufficient to reduce risk exposure, at least, to the levelin which “a consumer will be indifferent” between shopping via e-channels andvia physical channels. However, it might reach beyond just eliminating risks,and thereby becoming a benefit and increasing appetitive motivation.

to selling low cost, commodity products. This notion is chal-lenged in this research as the key premise of this research is thatperformance risk in e-commerce can be addressed by providinghigh-quality information that can facilitate the purchasing pro-cess to the same, if not higher level offered by physical channels.The next section presents the research model that demonstrateshow risks can be addressed with information quality, leading tothe substitution e-channels over physical channels for productpurchase.

IV. RESEARCH MODEL

The research model builds on the aforementioned literatureand takes a technological imperative perspective that views“technology as an exogenous force which determines or stronglyconstrains the behavior of individuals and organizations” [58].Consequently, technology characteristics (and behaviors in-duced by technology) are positioned as antecedents of infor-mation quality. We then justify the relationship between infor-mation quality and the choice of e-channels as a purchasingplatform. Table I summarizes the definitions of the constructsused for this study, while Fig. 1 shows the research model of theresearch.

A. Choice of E-Channel Switching from Physical to ElectronicChannels

A key transformation for consumers is to switch from pur-chasing through traditional physical channels to e-channels. Re-tail products offered through e-channels are often categorizedas either being: 1) experience goods “dominated by attributesfor which information search is more costly and/or difficult thandirect product experience,” or 2) search goods in which qualitycan be easily evaluated without consumption [64]. A productis regarded as an experience good when the product has moreexperience attributes than search attributes [81]. Importantly,if high-quality information can convert experience attributes ofa product into search attributes, consumers could essentially“experience” the product online before purchasing it. Indeed,what appears to be important in a consumer’s decision about e-channel choice is not the product type itself but the dominance


Fig. 1. Research model.

of searchable attributes in the presented information of the givenproduct. The primary reason why search goods are consideredappropriate for e-commerce transactions is that consumers al-ready have sufficient product information, mitigating or elimi-nating performance risk common to online transactions [3], [7].Even if a product is an experience good, high-quality informa-tion that renders the product’s characteristics searchable on e-channels should give consumers sufficient knowledge such thatphysically experiencing the product before purchase is not nec-essary. The dominance of the searchable attributes will reduceperformance risk that increases the probability of consumers tosubstitute e-channels for physical channels.

The benefits to the consumer is reducing the costs of con-ventional physical channels—inconvenience in terms of timeand travel to evaluate products, the slow pace of shopping, andthe lack of research aids within a traditional store. Consideringthese aspects, e-channels offer advantages and a better channelto purchase products. Yet, the biggest source of consumer dis-satisfaction in both channels is the unavailability of informationto dampen the perceived performance risk of consumers [8].If web technologies can enhance the quality of informationthrough technologically enhanced features, then consumers willbe less likely to require the physical experience of products,often used to reduce performance risk, and will more likelypurchase through e-channels [53]. Therefore, we hypothesizethat

H1: Information quality in e-channels has a positive effect on con-sumers’ choice of e-channels over physical channels for productpurchase.

B. Factors Affecting Information Quality in E-Channels

A key to purchasing over the Internet is the communi-cation process between consumers and sellers. Shannon and

Weaver’s [82] communication theory that defines the roles ofmessage, receiver, and sender offers a useful lens to understandthe role and antecedents of information quality. Consequently,we extract four antecedents or drivers of information qualitythrough the dimensions of telepresence and screening capabil-ity from the message dimension, cognitive overhead from thereceiver dimension, and channel trustworthiness from the senderdimension.

Media richness theory posits that richer information is re-quired under uncertain and equivocal situations [15]. While me-dia richness is known to have a close link with media choice, thatrelationship is moderated by perceived uncertainty and equiv-ocality [16] implying consumers seek a medium that conveysmore information to reduce uncertainty or equivocality. Here,more information means information richness rather than theamount of information to online consumers [17]. Since pur-chasing products through e-channels is accompanied by uncer-tainty and equivocality problems [18], rich product informationis needed to facilitate consumer’s evaluation efforts and the per-ception of high information quality. Richness can be achieved intwo dimensions: depth and breadth. Richness in terms of depthcan be represented by telepresence, and richness in terms ofbreadth may be represented by screening capability.

Telepresence is the experience of one’s physical environ-ment perceived through the mediation of a communicationmedium [85] or the presence which is “an illusion of beingthere in a mediated environment” [49, p. 25]. A high degree ofperceived telepresence comes from the mediated environmentthat “brings the experience and objects closer to us, allowingus to indirectly meet and experience other objects” [49, p. 18].Since experience goods are characterized by the dominanceof experience attributes, telepresence should be a key factorfor experience goods. Telepresence in e-channels is enabled byweb technologies that can provide vivid experiences through


tools such as interactive digital trial, 3-D experience, onlinecustomization [49], standardized specification [31], and easyaccess to an expert [84] or third party opinions [23]. Telepres-ence enabled by these tools will facilitate consumers’ productevaluation task. For example, 3-D experience enables consumersto zoom in and out, or turn a product just like in a physical storeevaluation. Therefore,

H2: Telepresence in e-channels has a positive effect on informationquality.

Screening capabilities offer customized information neededto search for products. Information on search goods is relativelyeasy to format and consumers know what information they arelooking for [3], leading to easy digitization and a vast selectionof information in e-channels. However, the attractiveness of avast selection of information available in e-channels is depen-dent on a consumer’s ability to sort and effectively manipulatethe information [1]. Therefore, consumers who want to pur-chase search goods through e-channels require useful screeningof the immense amount of information available in e-channels.A consumer’s desire to compare products and vendors, however,is not limited to just search goods. While attributes of an ex-perience good are mostly intangible and not easily searchable,experience attributes that are digitalized become searchable in-formation. Likewise, the quality of this information can be im-proved through filtering and sorting capabilities of consumers.For example, a powerful search agent on e-channels may giveto online consumers control to sort out products based on buyerpreference such as brand, price, size, color, etc. Therefore, thebetter the screening capability e-channels’ support, the more ef-fective e-channels can offer for consumers’ evaluative activities,leading to perceived higher quality information. Thus,

H3: Screening capability in e-channels has a positive effect on in-formation quality.

Cognitive overhead is the amount of mental activity at aninstance in time before processing a main task of informationsearch [88]. Information will not be considered of good qual-ity if the information is conveyed in a way that heightens thecognitive overhead of consumers. In the context of purchas-ing on the Internet, cognitive overhead is the additional mentalwork in making decisions concerning which links to follow andwhich to abandon and the large number of choices. A primarycause of cognitive overhead is known to be the “disorganizedinformation” or “noise” related to excessive information, dis-traction, interruption, and multitasking [41] as well as noiseoffered through pop-ups, cascading windows, inconsistent con-figurations, and too much information offered on the screen. Oneeffect when confronted with an overwhelming amount of infor-mation is information anxiety, resulting in the inability to inter-pret information [41]. High cognitive overhead overwhelms andfrustrates learners [29] and has a negative effect on consumers’perception of information quality. This suggests the followinghypothesis:

H4: The cognitive overhead of consumers has a negative effect oninformation quality.

The last component of communication is the source of in-formation, which is represented by channel trustworthinessin our context. McKnight et al. [62, p. 337] define trustwor-thiness as “a confident trustor perception that the trustee . . .has attributes that are beneficial to the trustor.” Serva et al.[80, p. 90] substitute “beneficial” with “benevolent” in this def-inition, noting that “benevolence. . .reflects the trustor’s beliefsthat the trustee has good intentions toward the trustor.” We definechannel trustworthiness as consumers’ beliefs that the web storehas good intentions to serve online consumers. Channel trust-worthiness along with consumer trust is asserted to be among thepivotal enabling forces of online exchanges under various riskssuch as uncertainty, lack of control, and anonymity of virtualshopping [4], [30].

Trustworthiness of a web store may reduce an online con-sumer concern about performance risk by enhancing perceivedinformation quality in e-channels.4 Halo effects and cognitiveconsistency theory underscore the importance of trustworthinessof an e-channel. Leuthesser et al. [48] found that a halo effectdecreases a consumer ability to discriminate among conceptu-ally distinct and potentially independent attributes, and resultsin higher ratings for individual attributes than the ratings wouldbe otherwise. Further, cognitive consistency theory states thatpeople tend to ignore or distort incompatible elements to arriveat a consistent view or to eliminate them with some objectivemeans [79]. Taken together, a positive perception about a ven-dor will trigger and uphold another positive perception aboutthe vendor’s behavior or the output of the behavior. In the ini-tial relationship, the perception of a website [60], vendor and/orbrand image [40], and/or vendor reputation [71] can engenderperceived trustworthiness of the e-channel. This trustworthi-ness of a web vendor (or a web store) brings forth halo effectsby rendering positive impressions for the information that thee-channel presents. Once trustworthiness is established, cogni-tive consistency theory suggests that a consumer will strive tomaintain a consistent set of beliefs in accordance with his/herexperience with a web store. This way, information from trust-worthy web stores is regarded as more valuable and reliable.Without perceiving trustworthiness of a web store, consumerswill not easily perceive the information carried on the store tobe useful or of high quality in their product evaluation. There-fore, we expect a positive relationship between channel trust-worthiness and information quality. This discussion leads to thefollowing hypothesis:

H5: Channel trustworthiness has a positive effect on informationquality.

C. Control Variables

Four constructs were controlled within this study. Three havealready been discussed, including system quality, service qual-ity, and comparative benefit (i.e., lower price, more convenience

4Within the e-commerce context, structural assurances such as security andinterpersonal trust such as vender trust are essential to attract e-channel transac-tions. The risk associated with these issues is source risk, which is addressed bysystem quality [2], [37], [69] and controlled for consumers’ choice of e-channelswithin our research model.


than in physical channels), which collectively itemized the ap-petitive motivation. Also, this study controls the level of productprice, defined as the perception of a high price of a product [52]that an individual consumer would have to pay given their finan-cial situation. Consumers may feel more performance risk whenbuying high-priced products [11], which will lead consumers tophysical channels where they can depend on human senses toreduce performance risk. Since the same amount of money willbe perceived differently depending on each consumer’s financialsituation, perceived price level rather than absolute price levelwill affect consumers’ channel choice decision.

V. RESEARCH METHOD

This study seeks general perceptions about e-channels notvendor-specific or product-specific perceptions. A commonapproach for this type of study is a field survey, in whichresearchers have participants imagine a website that theyrecently patronized and answer the survey questions [13].Instead, this study measured the perception of consumers oncontrolled/selected products on real websites. Four facultymembers in IS and statistical methods and two IS doctoral stu-dents from the Southeastern region of the U.S. were recruitedas panel members to initially evaluate that the websites met therequirements to be effectively used in the study. These require-ments are discussed next. The unit of analysis for this study isa consumer who searches for information about a product on awebsite.

A. Participants

Two conditions were imposed for participant selection: first,the participants must represent potential online consumers. Col-lege students in the Southeastern region of the U.S. met thiscondition and were selected for our study. While using col-lege students as participants is often questioned for studies withmanagerial contexts [63], researchers who study online behav-iors find undergraduate and graduate students active participantsin e-channel transactions, and represent the “next generation ofe-commerce users” [75]. Previous studies have found onlinepurchasers tend to be more educated and younger [46]. Con-sequently, students in many ways offer advantages for study-ing online consumer behaviors than employees in the businessfield [61]. The second condition was that participants have expe-rience in purchasing or at least searching for product informationonline. While encountering someone who has never used theInternet is rare [89], this lack of experience heightens the po-tential of computer or Internet anxiety.

B. Product Selection

Multiple products were selected that varied in price amongthe products. Data collected on these products were pooled foranalysis to reduce product-specific effects on channel choice.An initial pilot test (A) with 37 students was conducted to vali-date selection of experience goods. Six experience goods wereselected from previous e-commerce research: 1) digital cam-eras; 2) apparel; 3) books; 4) external hard drives; 5) flat-panel

LCD monitors; and 6) movie DVDs [13], [23], [46], [74]. Eachparticipant chose two products from the six on the list. Follow-ing Chiu et al.’s [13] procedure, participants initially respondedon their ability to evaluate the performance of each productwithout benefit of using the product. Participants were then sur-veyed about their potential ability to judge the performance afterexperiencing the product through information offered online us-ing a seven-point Likert scale. Products that scored low on thefirst scale and high on the second scale were considered an ex-perience good. Products that scored high on both scales wereregarded as a search good. Books and external hard drives werehigh on both scales in the pilot test and considered search goods.While there is no absolute delineating line between experienceand search goods, these items were eliminated from the pilot.The final list of the representative experience goods consisted ofdigital cameras [35], apparel [22], flat panel monitors (computerproducts [13]), and movie DVDs [74].

C. Website Selection

There were also two criteria for website selection. First, thewebsite had to be an actual business-to-customer (B2C) site thatconsumers used to search and/or buy products. Second, to ensurevariance in information quality, two websites were selected foreach product on the ends of the quality continuum: one withhigh and another with low information quality. Because theevaluation of what constitutes high and low quality is subjective,the appropriateness of the website selection was first assessed bypanel members to evaluate the potential variance of perceptionscores across websites. The selected websites were then pilottested for their score variance in a second pilot test.

In the second pilot test (B), two preselected websites for eachof the four products, again, one with high information qualityand the other with low information quality, were assessed by 30students in an undergraduate class who were randomly dividedinto eight groups, with each group consisting of three to fourmembers. A single product on a single website was randomlyassigned to each group. Each student was instructed to navigatethe designated website to search for product information forabout 5 min and then fill out the survey about their perceptionon the information quality and intention to purchase for about25 min. The goal of this pilot test was to find a score variance of atleast 1.0 (on a standard 7-point Likert scale) between the scoresof the high and low quality websites for each product.5 The resultwas that two pairs of websites6 out of the four preselected pairsshowed sufficient variance in perceived information quality. Thefour websites with little variance were dropped. After exploringfor website replacements, this pilot test was run again with adifferent set of websites. In the second run of the pilot test,a website from the four websites with sufficient variance inthe first pilot test later displayed “out of stock” for the targetproduct. Also, the description of another product was changed to

5We adopted a qualitative test. The average variance difference of 1 betweenhigh- and low-quality websites was accepted as sufficient unless the statisticalt-test for two population means said otherwise, which was used only as areference because of the insufficient power with small sample size.

6Websites for movie DVDs and for flat-panel LCD monitors


“refurbished” before the second pilot test (B). These websiteswere also replaced. Overall, six websites were replaced andtested again for the variance of information quality. A thirdpilot test was run with 30 students of three to five respondentsgrouped together. The four pairs of websites tested showed theaverage variance difference of more than 1.0 between high andlow information quality. Also, the tests of the mean differencewithin each pair of websites associated with a product resultedin the final eight websites, two for each product.

D. Instruments

Scale items were adapted from the literature except for onecontrol variable.7 All scale items were evaluated through con-tent validity checks and refinement by panel members and pilotparticipants. Participants were asked to indicate their agreementto each questionnaire using a seven-point Likert scale from“Not at all” (1) to “Very much” (7), from “Strongly disagree”to “Strongly agree,” or “Very unlikely” (1) to “Very likely” (7).

The two objectives of the final pilot test (C) were to assessthe content validity of the initial measures and refine individualitems if needed. After panel members initially checked the facevalidity of the items and their conformity to the domain of eachconstruct, 30 college students were recruited for this pilot study.They were divided into eight groups and filled out the surveyin the same manner as in pilot tests (A) and (B). In addition,respondents were asked to note ambiguity in item wordingswith recommended changes if necessary. Item reduction andrefinement were based on the comments and scores reported bythe respondents. Items with poor correlations in this pilot testwere discarded or reworded. As a result, 52 items were used tomeasure the 13 variables of the research model. The descriptionof these scale items is given as follows.

Consistent with the definition, choice of e-channels was oper-ationalized as the willingness of a consumer to substitute phys-ical channels with electronic channels for product purchase. In-dicators were adopted from Chiu et al. [13] and adapted to thisresearch context. According to Zmud [92], information qual-ity’s various dimensions tend to be perceived as a whole byconsumers, and information quality in a specific context may bebetter measured by the overall relevancy of the information to thecontext. Thus, information quality in this study was measuredby perceived relevancy of product information in purchasing ine-channels. Items were adopted with slightly modified word-ing from Xu and Koronis [91], Palmer [70], and McKinneyet al. [61]. Telepresence was operationalized as perceived vivid-ness of the product description in e-channels. Items are adoptedfrom Klein’s [43] instruments. An example for telepresencestates “Does the website enable you to find information aboutthe product as if you are in a physical store?” We measuredscreening capability in terms of the perceived degree of product-search facilitation in e-channels. Four items were driven from

7The items for product price did not exist, or the existing scales did not fitthe construct definitions. Consequently, items were developed for this construct.The development followed Nunnally’s [68] “domain sampling” method, whichrecommends the predefined domain of a construct and the selection of candidateitems represent this domain.

Kim et al.’s [38] instruments. Cognitive overhead was measuredin terms of the extent to which a consumer perceives mentaloverload arising from poorly organized information. Items thatreflect the combination of information overload and noise wereadapted from the instruments of Strong et al. [87] and Xu andKoronis [91]. Channel trustworthiness was measured throughitems drawn from Pennington et al. [71]. An exemplar itemstates “Do you find any reason to be cautious with this store?”

E. Survey Procedure and Biases to Be Controlled

Participations were voluntary. They were offered extra creditpoints to the respondent’s final score in the respective course.Participants were given a week to participate in this process sothey could choose a time and place for participation in the sur-vey at their convenience. The groups were randomly assignedto one of the eight websites for navigation. Next, each partic-ipant was instructed through email to navigate the designatedwebsite for at least 5 min and to look for the product informa-tion under the scenario where they were interested in buyingthe product. Data were collected when participants filled outsurvey questionnaires on a privately hosted website8 after nav-igating the designated website. While this procedure providedcontrol over survey variables, there still existed a possibility ofnonresponse bias and common method bias. The methodsadopted against nonresponse bias focused mostly on how todecrease nonresponse rate [79]. However, a recent study of Ro-gelberg et al. [76] reveals that the vast majority of nonresponseis passive in nature, and the passive nonresponse9 does not cre-ate bias unless “the survey assesses constructs that are indeedrelated to the reasons that passive nonrespondents fail to re-turn the survey.”10 Items of this study measure participants’intention and perception rather than their “laziness” or “busy-ness.” Therefore, nonresponse bias would not be as likely. Toavoid common method variance, which is frequent in studiesthat measure self-reported perception, we followed Podsakoffet al.’s [73] recommendations. First, three different types ofanchors were used for measurement (i.e., Strongly disagree,Not at all, Very unlikely). Second, scale items were separatedinto difference sections throughout the instruments. Addition-ally, comparative fit index difference between a model with andwithout a method factor was evaluated following Little’s [54]criterion to test the common method bias. Finally, discriminantvalidity was measured later in the data analysis.

VI. DATA ANALYSIS AND RESULTS

Overall, 341 students in 13 junior/senior level courses par-ticipated in the survey. 14 incomplete cases or cases with zerovariance across all the indicators were deleted. Additionally,18 outlier cases were deleted using the cutoff value of the

8http://www.surveymonkey.com9Passive nonresponse might occur when nonrespondents do not have access

to survey tools, forget about it, are ill, or busy [88], but they are not intentionallycommitted to no-participation [77].

10Rogelberg and Stanton [77] illustrate an example case when passive nonre-sponse could present a problem—“when the survey topic in question is relatedto workload, busyness, or excess demands.”


TABLE IIDESCRIPTION OF E-CHANNEL EXPERIENCE

Mahalanobis distance (α = 0.001). This resulted in a final countof 309 respondents in the sample. Maxwell’s [59] approach inthe psychological methodology literature calculates sample sizebased on a target power level and the estimate of partial effectsize, which can be calculated through correlation estimates. Thecalculated sample size to achieve a power of 0.80 was 235 forthe research model, which was met with the final sample size.11

This sample size was also tested for MacCallum et al.’s [55]power analysis for testing structural equation models (SEMs),and the expected power level approximated 1, which triangu-lates the adequacy of the sample size of this study.

Initial data found most participants had experience in usingthe Internet for product purchase (see Table II), a requirement ofthe sample. Participants’ experience ranged from 3 to 13 years(mean of 9.1 years), their ages ranged from 18 to 35 years old(the mean of 21.3 years old), and 40.1% of the total partici-pants were female. The composition of products on the desig-nated websites was 21% apparel, 21% movie/DVD, 29% digitalcamera, and 29% flat panel LCD monitor. Randomly assigningparticipants to groups of classes caused rates to differ for thevarious groups. While this uneven composition might rendera potential bias through product-specific effects, our analysis(described later) indicated that there was little product-specificeffect.

A. Testing for Common Method Variance

To check common method variance, a comparative fit index(CFI) difference test was performed comparing two measure-ment models: 1) a trait model with an added method factor and2) a trait-only model without any method factor [78]. The valueof CFI difference between the two was 0.012 (=0.967 − 0.955).The result was mixed because this value is a little higher than thecutoff of 0.01 recommended by Cheung and Rensvold [10] butfar less than Little’s [54] criterion of 0.05. Therefore, potential

11To estimate the correlations, studies similar to our study in investigatingonline behaviors were examined in the e-commerce literature. Ten randomarticles that reported correlation matrix were sampled, and both ρ2

xy and ρxx

were conservatively estimated as 0.4.

method bias was assessed in the structural model by compar-ing latent factors (trait loading) and the method factors (methodloading). The sizes of all trait loadings were bigger than those ofthe method loadings. The square root of trait average varianceextracted (AVE) (=0.909) was also greater than the square rootof method AVE (0.234). However, two12 out of 39 indicatorsshowed high loadings on a method factor, indicating that com-mon method bias potentially existed. Therefore, following therecent trend in IS research [51], we partitioned out the methodeffect by modeling a latent method factor within the structuralmodel in all subsequent analyses in this study. In this way, themethod effect was controlled, and causal relationships amongconstructs in the model could be tested with method effect beingheld constant.

B. Measurement Model

Given that all of the items were adapted from the literature(with the exception of two control variables), we employed aconfirmatory factor analysis approach using EQS 6.1 to testconstruct validity. All covariances among the latent variableswere included and estimated in the measurement model (forcovariance estimates, see Table III). The standard maximumlikelihood (ML) estimation in SEM assumes multivariate nor-mality. To check multivariate normality, Byrne [8] recommendstesting Mardia’s statistic and the normalized estimate. Mardia’sstatistic did not identify any outliers and observed data foreach individual construct met univariate normality. However,when all constructs in the model were pooled together, the testshowed multivariate nonnormality in the normalized estimate(=33.5).13 Therefore, following Byrne’s [8] recommendation,the ROBUST ML method was used to test both the measurementand the SEM.

Goodness of fit was examined to assess fit of the model tothe data. The fit indices of the measurement model (normedchi-square [χ2 /df] = 1.53, CFI = 0.96, SRMR = 0.04,

12IQ1 and IQ3.13Cutoff value: Klein [44] suggests less than 3.0, and Bentler [7] suggests

5.0.


TABLE IIICORRELATIONS AND AVE

TABLE IVFACTOR LOADINGS AND RELIABILITIES

RMSEA = 0.04) indicated a good fit given recommended cut-off values of χ2 /df less than 3 [44], CFI greater than 0.95 [33],SRMR less than 0.10 [44], and RMSEA less than 0.05 andwithin 90% CI of 0.04 and 0.05.14

To further validate the measurement model, the scale itemsconvergent and discriminant validity was tested. Convergent va-lidity was evaluated through Fornell and Larcker’s [24] threecriteria: 1) all measurement factor loadings exceeded 0.70;2) construct reliabilities exceeded 0.80; and 3) average vari-ance extracted (AVE) for each construct exceeded 0.50. Allfactor loadings exceeded the recommended 0.70 (see Table IV).Second, reliability measures were tested to show if the mea-surement scale is consistent. Cronbach’s coefficient alpha andcomposite reliability are the most commonly used estimates forinternal consistency of scale items [24], [44]. Both estimatesdemonstrated values over 0.80. Finally, AVE was assessed toidentify how much variance of the indicators is captured by theunderlying factor [24]. The AVE for each factor in the researchmodel met the required level (>0.5) for convergent validityranging between 0.72 and 0.91 (see Table III).

Discriminant validity can be assessed either by using thesquare root of AVE, which are the diagonal elements in the

14The rule of thumb for RMSEA is that RMSEA should be less than 0.05for a model to be a close approximate fit [41]. Also, if the lower and upperbounds of a 90% confidence interval (CI) of RMSEA are less than 0.05 and0.10, respectively, good approximate fit is strongly affirmed [41].

correlation matrices, or by checking multicollinearity. Discrim-inant validity is verified if the values of each diagonal ele-ment (square root of AVE) are greater than their correspondinghorizontal and vertical correlation coefficients [25]. Table IIIshows that all diagonal elements were larger than their cor-responding horizontal and vertical correlation coefficients. Wefurther checked multicollinearity, which can cause unreliable as-sessments of explanatory variables’ strength. Intercorrelationsamong the independent variables were below 0.80, indicating nomulticollinearity in the model [42]. Therefore, the discriminantvalidity of the factors in the research model was affirmed.

C. Structural Models

Since an acceptable measurement model was determined, co-variances in the measurement model were replaced with causalrelationships to test SEM. Several potential models with alter-nate paths were tested for fit to the data but did not improve


TABLE VFIT INDICES

TABLE VISEM RESULTS

upon the fit of the research model.15 The goodness of fit in-dices of the structural model (normed chi-square [χ2 /df] = 1.51,CFI = 0.97, SRMR = 0.04, RMSEA = 0.04) demonstrated agood fit given recommended cutoff values of χ2 /df less than3 [44], CFI greater than 0.95 [33], SRMR less than 0.10 [44],and RMSEA less than 0.05 and within 90% CI of 0.04 and0.05 [42]. A summary of the fit indices is in Table V.

All causal relationships in the research model were esti-mated through structural equation modeling using EQS 6.1.Coefficients for all paths were estimated together through theROBUST ML estimation. The model explains 54.6%16 of thevariance of choice e-channels over physical channels and findsthat information quality in e-channels has a significant ef-fect on choice of e-channels (standardized coefficient: 0.219;p < 0.01), as theorized in Hypothesis 1. Hypotheses 2 through5 are also supported, representing four antecedents to informa-tion quality in e-channels. These four factors of telepresence,screening capability, cognitive overhead, and channel trustwor-thiness explain 61.6% of the variance of information quality ine-channels. Table VI and Fig. 2 summarize the test results of thestructural model. For control variables, system quality, servicequality, and comparative benefit showed significant effects on

15For example, we added paths from the four antecedents of informationquality to choice of e-channels in the research model to check the fit of themediation model. This alternate model yielded a significantly worse fit to thedata (increase of chi-square by 1401 with 4 less df, normed chi-square [χ2 /df]= 6.10, CFI = 0.68, SRMR = 0.40, RMSEA = 0.12). For robustness check, wefurther tested R2 changes through hierarchical regression analysis, and addingeach variable in the model turned out to produce a significant R2 increase (seetable below).

16Including control variables.

choice of e-channels as expected; however, product price wasnot significant.

VII. DISCUSSION AND CONCLUSION

This research considers the importance of information qualityin choosing e-channels for purchase and makes the followingcontributions that are discussed next.

A. Research Implications

First, offering images with complementing text-based de-scriptions [52] is insufficient to make informed purchase de-cisions [36]. Instead, four drivers enhanced information qualitysufficiently for consumers to use the Internet beyond informa-tion search capabilities into purchasing. Telepresence providesa comprehensive and convincing interactive stimulus [65] thatsimulates experiencing products and leads to higher levels ofvalue by enhancing product information processed. In addition,channel trustworthiness increases the perception for informa-tion quality as confidence and reliance on the information in-creases [83]. However, care must be taken as consumers maytake peripheral routes in receiving information that reduce cog-nitive overhead (reducing effort and concentration) [72]. Con-sequently, screening capability to navigate such quantities ofinformation is the fourth driver of information quality.

Second, this study supports a simultaneous decision model.The classic sequential decision model17 suggests that one of the

17A widely accepted consumer decision model [21] presents the five-stage de-cision process: problem recognition, information search, alternative evaluation,choice, and outcomes.


Fig. 2. SEM results of the research model.

important decisions made in the choice stage, the selection ofa purchasing channel, is isolated from the earlier informationsearch stage. However, recent research suggests that processessuch as information search and choice could occur simultane-ously [86]. This study finds that choice of a purchasing channelis made as consumers are searching for product information andsupporting the simultaneous occurrence of processes, especiallyin the network-enabled e-commerce setting. A critical issue inthis argument is how consumers link the evaluation aspects of theinformation search to the choice aspects of purchasing channelswhen the two processes occur in parallel. This study highlightsinformation quality as the link between information search andchoice of purchasing channels.

Third, the findings of this study contribute to the knowledgemanagement literature as high-quality information in e-channelssuccessfully digitizes experience attributes of a product, lead-ing consumers to purchase products online without the needfor physical evaluation. Consequently, factors affecting qual-ity could be traced to the core communicative dimensions ofmessage, receiver, and sender. This study finds that consumerstend to perceive high quality of information when the informa-tion in e-channels demonstrates high telepresence and screeningcapability, imposes little cognitive overhead, and comes froma trustworthy website. Considering that these factors help indigitalizing intangible experience attributes into tangible prod-uct knowledge, this study illustrates the communicative aspectsof information (and knowledge). While knowledge managementusually focuses on organizational activities, this study illustrates“how” and “what” of knowledge management at the individualconsumer level. That is, consumers’ desire to reduce risk intransactions demands tangible product knowledge. Our studydemonstrates that IT facilitates the conversion of intangible ex-perience attributes into tangible digitized information and makesthe search for experience goods easier for the consumers.

Finally, this research highlights the need for focusing on thechoice of e-channels for purchasing by using a clearly de-fined dependent variable of e-channel choice. The domain ofe-channel choice has been considered through three differentperspectives: 1) the choice among individual websites; 2) thechoice of whether to accept the Internet as a purchasing chan-nel; and 3) the choice between electronic and physical channels.Using inconsistent descriptions of e-channel choice thwarts cu-mulative theory development. Focusing on the third perspective,this research considers the choice of e-channels over physicalchannels and the role of information quality in the consumer’sdecision to use e-channels as a purchasing place beyond its roleas an information source, thereby substituting physical channels.

B. Practical Implications

Online sales continue to grow as a portion of U.S. retailsales. E-retailers can increase the volume of online transac-tions by: 1) making sure those consumers who search for prod-uct information use the Internet for online purchasing and/or2) expanding the pool of products sold in e-channels. The re-sults of our study corroborate the pivotal role of informationquality in these options.

This study found four effective levers to increase infor-mation quality and the number of consumers who use theInternet beyond information searching to purchasing. Estab-lishing trustworthiness through reputation and taking advantageof web technologies that enhance telepresence (such as virtualreality) has a positive effect on consumers’ perception of infor-mation quality. However, as more information is used to enhancetelepresence, it must be balanced with enhanced screening ca-pability. Otherwise, the benefit of increasing telepresence mightbe overpowered by the negative consequences of cognitive over-head [61].


TABLE VIIKEY CONTRIBUTIONS AND IMPLICATIONS

Also, the product pool may be expanded through high-qualityinformation that converts experience attributes into search at-tributes. High information quality tends to lower performancerisk, which is an important issue in online purchase environ-ments [6]. The direct effect of information quality on e-channelchoice found in this study supports the argument that should-be-experienced attributes may be converted into searchable at-tributes via digitized information. This contradicts the notionthat experience goods require physical examination and, conse-quently, are not particularly suitable for online shopping. Rather,it is not the product’s physical attributes but the quality ofproduct information that determines the adequacy of onlinepurchasing. Table VII summarizes keycontributions andimplicaions.

C. Future Research and Limitations

There are several potential research opportunities to extendthe contributions of this study. First, future research can ex-amine online purchase of services through the investigation ofthe roles and the determinants of information quality. Internettechnologies not only shorten the distance between buyers andsellers but also enable rich communication, approximating face-to-face conversation. The future of service transactions on theInternet appears particularly promising and owing further study.Second, the result of our study does not automatically lead sell-ers to pursue active use of innovative web technologies that po-tentially improve the information quality on their online stores.The domain of information to be transferred to consumers, andthe timing and extent of information exposure will depend onvarious contingencies, such as the market power of the company,characteristics of target consumers, and the national averagebandwidth for consumers. If a company wants to limit the usee-channels as only an information source (e.g., to mitigate can-nibalizing the sales of their physical stores), the company shouldnot offer high-quality information to consumers in their onlinestores. Therefore, our study can be extended to investigate thedeterminants of firm strategy on the levels of information expo-sure and quality. Third, since telepresence offers virtual experi-ence to consumers, it may even be possible for online stores topresent higher quality information than brick-and-mortar stores.Consumers might have access to a certain facet of informationthat used to be unavailable in a physical space because of phys-ical limitations. For example, home buyers cannot take a lookat the details of a house that is under construction. However,

3-D graphics can create a virtual house where home buyerscan examine and experience the details of the house, and evenmake customized requests to home builders by screening vari-ous options such as number of rooms and square feet of garden.Therefore, this enhanced telepresence and screening capabilityof virtual space might operate as motivators, thereby enablingbetter consumer experience in electronic channels than physicalchannels. Finally, in the pilot study for product selection, movieDVDs were perceived as experience goods while books wereclassified as search goods. By nature, it is not easy to evalu-ate these products before consumers watch or read the wholecontent of these products, which makes experience goods to bea natural categorization for these products. This peculiarity ofcategorizing books as search goods might be due to the factthat students might have used their most familiar books liketextbooks for this pilot study. A student may have a limited cu-riosity in a textbook, as often purchase books for classes withonly the title before reading the textbook. A further study oncategorization of products is warranted with a variety of generalconsumers.

D. Conclusion

Competition between electronic and physical channels hasintensified in the Internet era. This study attempts to identify, aswell as explain this deterrent in online purchasing. This studyis a first step in developing the comprehensive nomological net-work of e-channel choice. While information quality has beenone of the least examined concepts in the e-commerce liter-ature, we identified it as key to capturing customers who willpurchase through e-channels. Consequently, a theoretical modelthat explains the consumers’ choice of electronic channels wasdeveloped and tested. Results provide support for the model,thereby illustrating information quality’s ability to address per-formance risk. Overall, this study directs practitioners’ attentionto the reasons why information quality is important in electronictransactions, and contributes to the understanding of the IT arti-facts through the identification of the four drivers of informationquality in e-channels. This study also opens potential opportu-nities for future studies on such topics as detailed description oftelepresence, identification of nomological network about ser-vice quality and systems quality in e-channels, and determinantsof online service purchase. We hope this study serves as a cat-alyst to facilitate more and better studies in information qualityin an Internet purchasing context.


APPENDIXLITERATURE REVIEW ON E-CHANNEL CHOICE

TABLE ASTUDIES ON CHOICE AMONG INDIVIDUAL WEBSITES18

TABLE BSTUDIES ON INTENTION TO CHOOSE TO USE E-CHANNELS


TABLE CSTUDIES ON CHOICE OF E-CHANNELS OVER TRADITIONAL CHANNELS

REFERENCES

[1] J. Alba and J. Lynch, “Interactive home shopping: consumer, retailer,and manufacturer incentives to participate in electronic marketplaces,” J.Marketing, vol. 61, no. 3, pp. 38–53, 1997.

[2] S. Ba, J. Stallaert, A. Whinston, and H. Zhang, “Choice of transactionchannels: The effects of product characteristics on market evolution,” J.Manag. Inf. Syst., vol. 21, no. 4, pp. 173–197, 2005.

[3] T. Barron and A. N. Saharia, “Optimal information structures for the sellerof a search good,” Inf. Syst. Res., vol. 1, no. 2, pp. 188–204, 1990.

[4] A. Bhattacherjee, “Individual trust in online firms: Scale development andinitial test,” J. Manag. Inf. Syst., vol. 19, no. 1, pp. 211–241, 2002.

[5] A. Bhatnagar, S. Misra, and H. R. Rao, “On risk, convenience, and internetshopping behavior,” Commun. ACM, vol. 43, no. 11, pp. 98–105, 2000.

[6] R. R. Burke, “Technology and the customer interface: What consumerswant in the physical and virtual store,” J. Acad. Market. Sci., vol. 30,no. 4, pp. 411–432, 2002.

[7] R. E. Burroughs and R. Sabherwal, “Determinants of retail electronicpurchasing: A multi-period investigation,” INFOR, vol. 40, no. 1, pp. 35–56, 2001.

[8] B. M. Byrne, Structural Equation Modeling with EQS: Basic Concepts,Applications, and Programming, 2nd ed. Mahwah, NJ: Lawrence Erl-baum Associates, Inc., 2006.

[9] T. Chantal (2009, Feb. 2). E-commerce is flat as a percentage of totalretail sales. [Online]. Available: http://www.dmnews.com/e-commerce-is-flat-as-a-percentage-of-total-retail-sales/article/126725/.

[10] A. Chaudhuri and M. B. Holbrook, “The chain effects from brand trustand brand affect to brand performance: The role of brand loyalty,” J.Market., vol. 652, pp. 81–93, 2001.

[11] M. T. Cheung and Z. Liao, “Supply-side hurdles in Internet B2C e-commerce: An empirical investigation,” IEEE Trans. Eng. Manag.,vol. 50, no. 4, pp. 458–469, Nov. 2003.

[12] G. W. Cheung and R. B. Rensvold, “Evaluating goodness-of-fit indexesfor testing measurement invariance,” Struct. Eq. Modeling, vol. 9, no. 2,pp. 233–255, 2002.

[13] H. Chiu, Y. Hsieh, and C. Kao, “Website quality and customer’s behavioralintention: An exploratory study of the role of information,” Total Qual.Manag. Bus. Excel., vol. 16, no. 2, pp. 185–197, 2005.

[14] J. Conklin, “Hypertext: An introduction and survey,” Comput. Mag.,vol. 20, pp. 17–41, 1987.

[15] R. L. Daft and R. H. Lengel, “Organizational information requirements,media richness and structural design,” Manag. Sci., vol. 32, no. 5, pp. 554–571, 1986.

[16] J. D’Ambra, R. E. Rice, and M. O’Connor, “Computer-mediated commu-nication and media preference: An investigation of the dimensionality ofperceived task equivocality and media richness,” Behav. Inf. Technol.,vol. 17, no. 3, pp. 164–174, 1998.

[17] W. H. DeLone and E. R. McLean, “Measuring e-commerce success: Ap-plying the DeLone and McLeanInformation systems success model,” Int.J. Electron. Comm., vol. 9, no. 1, pp. 31–47, 2004.

[18] A. R. Dennis, “Rethinking media richness: Toward a theory of mediasynchronicity,” presented at the Proc. 32nd Hawaii Int. Conf. SystemSciences, Maui, HI, 1999.

[19] A. Dickinson and J. M. Pearce, “Inhibitory interactions between appetitiveand aversive stimuli,” Psychol. Bulletin, vol. 84, pp. 690–711, 1977.

[20] G. W. Dickson, J. A. Senn, and N. L. Chervany, “Research in managementinformation systems: The Minnesota experiments,” Manag. Sci., vol. 23,no. 9, pp. 913–923, Sep. 1977.

[21] J. Etezadi-Amoli and A. F. Farhoomand, “On end-user computing satis-faction,” MIS Quart., vol. 15, no. 1, pp. 1–4, 1991.

[22] A. M. Fiore, H. Jin, and J. Kim, “For fun and profit: Hedonic valuefrom image interactivity and responses toward an online store,” Psychol.Market., vol. 22, no. 8, pp. 669–694, 2005.

[23] G. Ford, D. B. Smith, and J. L. Swasy, “An empirical test of the search,experience and credence attributes framework,” in Advances in ConsumerResearch, M. J. Houston, Ed. Provo, UT: Association for ConsumerResearch, 1988, pp. 239–243.

[24] C. Fornell and D. F. Larcker, “Evaluating structural equations with unob-servable variables and measurement error,” J. Market. Res., vol. 18, no. 2,pp. 39–50, 1981.

[25] D. Gefen, D. Straub, and M. Boudreau, “Structural equation modeling andregression: Guidelines for research practice,” Commun. Assoc. Inf. Syst.,vol. 4, pp. 1–79, Aug. 2000.

[26] D. Gefen, E. Karahanna, and D. W. Straub, “Trust and TAM in onlineshopping: An integrated model,” MIS Quart., vol. 27, no. 1, pp. 51–90,2003.

[27] A. Gupta, B. Su, and Z. Walter, “An empirical study of consumer switch-ing from traditional to electronic channels: A purchase-decision pro-cess perspective,” Int. J. Electron. Comm., vol. 8, no. 3, pp. 131–161,2004.

[28] K. Hassanein and M. Head, “The impact of infusing social presence in theweb interface: An investigation across product types,” Int. J. Electron.Comm., vol. 10, no. 2, pp. 31–55, 2005.

[29] J. Herrod. (2000, Dec.) Cognitive Overload. [Online]. Availablehttp://www.jchconsulting.com/fall2000/index.htm

[30] D. L. Hoffman, T. P. Novak, and M. Peralta, “Building consumer trustonline,” Commun. ACM, vol. 42, no. 4, pp. 80–85, 1999.

[31] P. Hopkinson, A. Sammut, and M. Whitaker, “The standardisation ofenvironmental performance indicators and their relationship to corporateenvironmental reporting: What can we learn from the UK water industry?”J. Environ. Assess. Pol. Manag., vol. 1, no. 3, pp. 277–296, 1999.

[32] Y. Hsieh, H. Chiu, and M. Chiang, “Maintaining a committed online cus-tomer: A study across search-experience-credence products,” J. Retailing,vol. 81, no. 1, pp. 75–82, 2005.


[33] J. S. Ilfeld and R. S. Winer, “Generating website traffic,” J. AdvertisingRes., vol. 42, no. 5, pp. 49–61, 2002.

[34] M. C. W. Janssen and V. D. R. Noll. (2002). “Electronic commerce andretail channel substitution,” Tinbergen Institute Working Paper No. 02-042/1 [Online]. Available: http://ssrn.com/abstract=309679

[35] Z. Jiang, W. Wang, and I. Benbasat, “Online consumer decision support,”Commun. ACM, vol. 48, no. 9, pp. 92–98, 2005.

[36] Z. Jiang and I. Benbasat, “The effects of presentation formats and taskcomplexity on online consumers’ product understanding,” MIS Quart.,vol. 31, no. 3, pp. 475–500, 2007.

[37] R. L. Keeney, “The value of internet commerce to the customer,” Manag.Sci., vol. 45, no. 4, pp. 533–542, 1999.

[38] Y. J. Kim, R. Kishore, and G. L. Sanders, “From DQ to EQ: Understandingdata quality in the context of e-business systems,” Commun. ACM, vol. 48,no. 10, pp. 75–81, 2005.

[39] R. C. King, R. Sen, and M. Xia, “Impact of web-based e-commerce onchannel strategy in retailing,” Int. J. Electron. Commerce, vol. 8, no. 3,pp. 103–130, 2004.

[40] A. Kirmani and A. R. Rao, “No pain, no gain: A critical review of theliterature on signaling unobservable product quality,” J. Market., vol. 64,no. 2, pp. 66–79, 2000.

[41] D. Kirsh, “A few thoughts on cognitive overload,” Intellectica, vol. 30,pp. 19–51, 2000.

[42] L. R. Klein, “Evaluating the potential of interactive media through anew lens: Search versus experience goods,” J. Bus. Res., vol. 41, no. 3,pp. 195–203, 1998.

[43] L. R. Klein, “Creating virtual product experiences: The role of telepres-ence,” J. Interactive Marketing, vol. 17, no. 1, pp. 41–55, 2003.

[44] R. B. Klein, Principles and Practice of Structural Equation Modeling,2nd ed. New York: Guilford, 2005.

[45] J. Konorski, Integrative Activity of the Brain. Chicago, IL: Univ.Chicago Press, 1967.

[46] S. E. Kovar, K. G. Burke, and B. R. Kovar, “Consumer responses to theCPA web trust assurance,” J. Inf. Syst., vol. 14, no. 1, pp. 17–35, 2000.

[47] Y. W. Lee, D. M. Strong, B. K. Kahn, and R. Y. Wang, “AIMQ: A method-ology for information quality assessment,” Inf. Manag., vol. 40, no. 2,pp. 133–146, 2002.

[48] L. Leuthesser, C. S. Kohli, and K. R. Harich, “Brand equity: The haloeffect measure,” Eur. J. Market., vol. 29, no. 4, pp. 57–66, 1995.

[49] H. Li, T. Daughty, and F. Biocca, “Characteristics of virtual experience inelectronic commerce: A protocol analysis,” J. Interact. Market., vol. 15,no. 3, pp. 13–30, 2001.

[50] X. Li, G. Rong, and J. B. Thatcher, “Swift trust in Web vendors: The roleof appearance and functionality,” J. Organ. End-User Comput., vol. 21,no. 1, pp. 88–108, 2009.

[51] H. Liang, N. Saraf, Q. Hu, and Y. Xue, “Assimilation of enterprise sys-tems: The effect of institutional pressures and the mediating role of topmanagement,” MIS Quart., vol. 31, no. 1, pp. 59–87, 2007.

[52] D. R. Lichtenstein, S. Burton, and E. J. Karson, “The effect of semanticcues on consumer perceptions of reference price ads,” J. Consum. Res.,vol. 18, no. 3, pp. 380–391, Dec. 1991.

[53] J. Lin and H. C. Chan, “Understanding the beliefs and intentions in searchand purchase functions in an E-commerce web site,” IEEE Trans. Eng.Manag., vol. 56, no. 1, pp. 106–114, Feb. 2009.

[54] T. D. Little, “Mean and covariance structures (MACS) analyses of cross-cultural data: Practical and theoretical issues,” Multivariate Behav. Res.,vol. 32, pp. 53–76, 1997.

[55] R. C. MacCallum, M. W. Browne, and H. M. Sugawara, “Power analysisand determination of sample size for covariance structure modeling,”Psychol. Methods, vol. 1, no. 2, pp. 130–149, 1996.

[56] V. Mahajan, R. Srinivasan, and J. Wind, “The Dot.com retail failures of2000: Were there any winners?” J. Acad. Market. Sci., vol. 30, no. 4,pp. 474–486, 2002.

[57] M. A. Mahmood, K. Bagchi, and T. C. Ford, “On-line shopping behavior:Cross-country empirical research,” Int. J. Electron. Comm., vol. 9, no. 1,pp. 9–30, 2004.

[58] M. L. Markus and D. Robey, “Information technology and organizationalchange: Causal structure in theory and research,” Manag. Sci., vol. 34,no. 5, pp. 583–598, May 1988.

[59] S. E. Maxwell, “Sample size and multiple regression analysis,” Psychol.Methods, vol. 5, no. 4, pp. 434–458, 2000.

[60] D. E. McCorkle, “The role of perceived risk in mail order catalog shop-ping,” J. Direct Market., vol. 4, pp. 26–35, Autumn 1990.

[61] V. McKinney, Y. Kanghyun, and F. M. Zahedi, “The measurement of web-customer satisfaction: An expectation and disconfirmation approach,” Inf.Syst. Res., vol. 13, no. 3, pp. 296–315, 2002.

[62] D. H. McKnight, C. Vivek, and K. Charles, “Developing and validatingtrust measures for e-commerce: An integrative typology,” Inf. Syst. Res.,vol. 13, no. 3, pp. 334–362, 2002.

[63] S. M. Miranda and C. S. Saunders, “The social construction of meaning:An alternative perspective on information sharing,” Inf. Syst. Res., vol. 14,no. 1, pp. 87–106, 2003.

[64] P. J. Nelson, “Advertising as information,” J. Political Economy, vol. 82,no. 4, pp. 729–754, 1974.

[65] M. R. Nelson, R. A. Yaros, and H. Keum, “Examining the influenceof telepresence on spectator and player processing of real and fictitiousbrands in a computer game,” J. Advertising, vol. 35, no. 4, pp. 87–99,2006.

[66] R. Nikolaeva, “Strategic determinants of web site traffic in on-line retail-ing,” Int. J. Electron. Commerce, vol. 9, no. 4, pp. 113–132, 2005.

[67] A. Noteberg, E. Christiaanse, and P. Wallage, “Consumer trust in electronicchannels,” e-Service J., vol. 2, no. 2, pp. 46–67, 2003.

[68] J. Nunnally, Psychometric Theory. New York: McGraw-Hill, 1978.[69] A. O’Cass and T. Fenech, “Web retailing adoption: exploring the nature

of internet users Web retailing behaviour,” J. Retailing Consum. Serv.,vol. 10, no. 2, pp. 81–94, 2003.

[70] J. W. Palmer, “Web site usability, design, and performance metrics,” Inf.Syst. Res., vol. 13, no. 2, pp. 151–167, 2002.

[71] R. Pennington, H. D. Wilcox, and V. Grover, “The role of system trust inbusiness-to-consumer transactions,” J. Manag. Inf. Syst., vol. 20, no. 3,pp. 197–226, 2003.

[72] R. E. Petty and J. T. Cacioppo, “Source factors and the elaboration likeli-hood model of persuasion,” Adv. Consum. Res., vol. 11, no. 1, pp. 668–672, 1984.

[73] P. M. Podsakoff, S. B. MacKenzie, J. Y. Lee, and N. P. Podsakoff, “Com-mon method biases in behavioral research: A critical review of the liter-ature and recommended remedies,” J. Appl. Psychology, vol. 88, no. 5,pp. 879–903, 2003.

[74] D. A. Reinstein and C. M. Snyder, “The influence of expert reviews onconsumer demand for experience goods: A case study of movie critics,”J. Ind. Econ., vol. 53, no. 1, pp. 27–51, 2005.

[75] N. J. Rifon, R. LaRose, and S. M. Choi, “Your privacy is sealed: Effectsof privacy seals on trust and personal disclosures,” J. Consum. Affairs,vol. 39, no. 2, pp. 337–360, 2005.

[76] S. G. Rogelberg, J. M. Conway, M. E. Sederburg, C. Spitzmuller, S. Aziz,and W. E. Knight, “Profiling active and passive-non-respondents to anorganizational survey,” J. Appl. Psychol., vol. 88, no. 6, pp. 1104–1114,2003.

[77] S. G. Rogelberg and J. M. Stanton, “Introduction understanding anddealing with organizational survey nonresponse,” Organ. Res. Methods,vol. 10, no. 2, pp. 195–209, 2007.

[78] A. Satorra and P. M. Bentler, “A scaled difference chi-square test statisticfor moment structure analysis,” Psychometrika, vol. 66, no. 4, pp. 507–514, 2001.

[79] T. T. Selvarajan, “A cognitive processing model for assessing ethicalbehaviour of employees,” J. Amer. Acad. Bus., vol. 9, no. 1, pp. 86–92,2006.

[80] M. A. Serva, J. S. Benmati, and M. A. Fuller, “Trustworthiness in B2Ce-commerce: An examination of alternative models,” ACM SIGMISDatabase, vol. 36, no. 3, pp. 89–108, 2005.

[81] C. Shapiro and H. R. Varian, Information Rules: A Strategic Guide to theNetwork Economy. Boston, MA: Harvard Business School Press, 1999,p. 9.

[82] C. E. Shannon and W. Weaver, A Mathematical Model of Communication,Urbana, IL: Univ. Illinois Press, 1949.

[83] M. Spence, “Signaling in retrospect and the informational structure ofmarkets,” Amer. Econ. Rev., vol. 92, no. 3, pp. 434–459, 2002.

[84] L. A. Steelman, P. E. Levy, and A. F. Snell, “The feedback environmentscale: Construct definition, measurement, and validation,” Edu. Psychol.Meas., vol. 64, no. 1, pp. 143–164, 2004.

[85] J. Steuer, “Defining virtual reality: Dimensions determining telepresence,”J. Commun., vol. 42, pp. 73–93, Autumn 1992.

[86] D. W. Stewart, G. L. Frazier, and I. Martin, “Integrated channel manage-ment: Merging the communication and distribution functions of the firm,”in Integrated Communication: Synergy of Persuasive Voices, E. Thorsonand J. More, Eds. Mahwah, NJ: Lawrence Erlbaum Associates, 1996,pp. 185–215.

[87] D. M. Strong, Y. W. Lee, and R. Y. Wang, “Data quality in context,”Commun. ACM, vol. 40, no. 5, pp. 103–110, 1997.

[88] J. Sweller, “Cognitive load theory, learning difficulty and instructionaldesign,” Learn. Instruction, vol. 4, pp. 295–312, 1994.


[89] U.S. Census Bureau. (2009). The 2009 Statistical Abstract of the UnitedState, 128th ed., Washington, D.C., Tables 1013, 1120, and 1122 [Online].Available: http://www.census.gov/statab/www/.

[90] R. Y. Wang and D. M. Strong, “Beyond accuracy: What data quality meansto data consumers,” J. Manag. Inf. Syst., vol. 12, no. 4, pp. 5–34, 1996.

[91] H. Xu and A. Koronios, “Understanding information quality In E-business,” J. Comput. Inf. Syst., vol. 45, no. 2, pp. 73–82, 2004.

[92] R. W. Zmud, “An empirical investigation of the dimensionality of theconcept of information,” Decis. Sci., vol. 9, no. 2, pp. 187–195, 1978.

Jaejoo Lim received the Ph.D. degree in informationsystems from Clemson University, SC.

He is an Assistant Professor of MIS at South Car-olina State University, Orangeburg. His research in-terests include various aspects of information qual-ity, e-commerce strategies and applications, IT valueand investment, and IT assimilation process. He haspublished in many academic journals including De-cision Sciences, Information & Management, Com-munications of the AIS, Journal of the AIS, Journalof Information Technology Management, and various

conference proceedings. He recently received the 1890 Evans-Allen researchgrant funded by NIFA.

Varun Grover received the Ph.D. degree from the University of Pittsburgh, PA.He is the William S. Lee (Duke Energy) Distinguished Professor of informa-

tion systems at Clemson University, Clemson, SC. He has published extensivelyin the information systems field, with more than 200 publications in major refer-eed journals. Nine recent articles have ranked him among the top four researchersbased on the number of publications in the top Information Systems journals,as well as citation impact (h-index). He is currently working in the areas ofIT value, system politics, and process transformation and recently released histhird book (with M. Lynne Markus) on process change.

Dr. Grover is a Senior Editor (Emeritus) for the MIS Quarterly, the Journalof the AIS, and the Database. He is the recipient of numerous awards fromUSC, Clemson, AIS, DSI, Anbar, PriceWaterhouse, etc., for his research andteaching. He is a Fellow of the Association for Information Systems.

Russell L. Purvis received the Ph.D. degree from Florida State University, in1994.

He is an Associate Professor of information systems at Clemson University,Clemson, SC. His current research interests include organizational transforma-tion through information technologies, project management, and informationtechnology implementation. He has published extensively in academic journalsincluding IEEE TRANSACTIONS IN ENGINEERING MANAGEMENT, IEEE TRANS-ACTIONS ON SYSTEMS, MAN, AND CYBERNETICS,MANAGEMENT SCIENCE, MISQuarterly, and Organization Science, among others.

He is the recipient of teaching and research awards from the University ofCentral Florida and Clemson University.

Documents

348 IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT, … · one willbuy[26].Psychological riskisdeﬁned asconcern about self-image that is reﬂected on the purchased product [53]. The